Image-based wavefront sensing for astronomy using neural networks

• Motivated by the potential of non-diffraction limited, real-time computational image sharpening with neu7 ral networks in astronomical telescopes, we have studied wavefront sensing with convolutional neural networks basedon a pair of in-focus and out-of-focus point spread functions. By simulation, we generated a large dataset for trainingand validation of neural networks, and trained several networks to estimate Zernike polynomial approximations forthe incoming wavefront. We included the effect of noise, guide star magnitude, blurring by wide band imagining, andbit depth. We conclude that the “ResNet” works well for our purpose, with a wavefront RMS error of 130 nm forr0 = 0.3 m, guide star magnitudes 4–8, and inference time of 8 ms. It can also be applied for closed-loop operation inan adaptive optics system. We also studied the possible use of a Kalman filter or a recurrent neural network and foundthat they were not beneficial to performance of our wavefront sensor

Subject headings

NATURVETENSKAP  -- Fysik -- Astronomi, astrofysik och kosmologi (hsv//swe)

NATURAL SCIENCES  -- Physical Sciences -- Astronomy, Astrophysics and Cosmology (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Rymd- och flygteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Aerospace Engineering (hsv//eng)

Keyword

optics

astronomy

telescope

wavefront sensor

neural network

image sharpening

Atmosfärsvetenskap

Atmospheric science

Neural networks

Point spread functions

Wavefront sensors

Wavefronts

Stars

Astronomy

Telescopes

V-band

Error analysis

Publication and Content Type

ref (subject category)

art (subject category)